Reduced-Complexity ML Detection and Capacity-Optimized Training for Spatial Modulation Systems

Spatial Modulation (SM) is a recently developed low-complexity Multiple-Input Multiple-Output scheme that jointly uses antenna indices and a conventional signal set to convey information. It has been shown that the Maximum-Likelihood (ML) detector of an SM system involves joint detection of the tran...

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Vydáno v:IEEE transactions on communications Ročník 62; číslo 1; s. 112 - 125
Hlavní autoři: Rajashekar, Rakshith, Hari, K. V. S., Hanzo, L.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.01.2014
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Allocations
and channel estimation
Antennas
Applied sciences
Channel estimation
Codes
Communication channels
Complexity
Computational complexity
Detection, estimation, filtering, equalization, prediction
Detectors
Exact sciences and technology
Information, signal and communications theory
ML decoding
Modulation
Modulation, demodulation
Optimization
Radiocommunications
Receivers
Searching
Signal and communications theory
Signal, noise
Spatial modulation
Systems, networks and services of telecommunications
Telecommunications
Telecommunications and information theory
Training
Transmission and modulation (techniques and equipments)
Transmitting antennas
Wideband communications
Performance evaluation
MIMO system
Parameter estimation
Quadrature amplitude modulation
Worst case method
Measurement sensor
ML decoding
training
Power allocation
Computational complexity
Information transmission
Optimization
Maximum likelihood decoding
Joint detection
Learning
Simulation
Spatial modulation
Optimal allocation
Transmitting antenna
Channel estimation
Maximum likelihood
Signal to noise ratio
ISSN:0090-6778, 1558-0857
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Popis
Shrnutí:Spatial Modulation (SM) is a recently developed low-complexity Multiple-Input Multiple-Output scheme that jointly uses antenna indices and a conventional signal set to convey information. It has been shown that the Maximum-Likelihood (ML) detector of an SM system involves joint detection of the transmit antenna index and of the transmitted symbol, hence, the ML search complexity grows linearly with the number of transmit antennas and the size of the signal set. To circumvent the problem, we show that the ML search complexity of an SM system may be rendered independent of the constellation size, provided that the signal set employed is a square- or a rectangular-QAM. Furthermore, we derive bounds for the capacity of the SM system and derive the optimal power allocation between the data and the training sequences by maximizing the worst-case capacity bound of the SM system operating with imperfect channel state information. We show, with the aid of our simulation results, that the proposed detector is ML-optimal, despite its lowest complexity amongst the existing detectors. Furthermore, we show that employing the proposed optimal power allocation provides a substantial gain in terms of the SM system's capacity as well as signal-to-noise ratio compared to its equal-power-allocation counterpart. Finally, we compare the performance of the SM system to that of the conventional Multiple-Input Multiple-Output (MIMO) system and show that the SM system is capable of outperforming the conventional MIMO system by a significant margin, when both the systems are employing optimal power splitting.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2013.120213.120850